{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_sim = pd.DataFrame(\n",
    "    {\n",
    "        'Kilometres': [1, 2, 3, 4, 5],\n",
    "        'Meter': [7.5, 10, 12.5, 15, 17.5],\n",
    "        'Paid (incl. tips)': [8, 10, 14, 15, 20],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Meter</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kilometres</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Meter\n",
       "Kilometres       \n",
       "1             7.5\n",
       "2            10.0\n",
       "3            12.5\n",
       "4            15.0\n",
       "5            17.5"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sim.set_index('Kilometres')[['Meter']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Meter</th>\n",
       "      <th>Paid (incl. tips)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kilometres</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.5</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Meter  Paid (incl. tips)\n",
       "Kilometres                          \n",
       "1             7.5                  8\n",
       "2            10.0                 10\n",
       "3            12.5                 14\n",
       "4            15.0                 15\n",
       "5            17.5                 20"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sim.set_index('Kilometres')[['Meter', 'Paid (incl. tips)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:20: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(16, 5), sharex=False, sharey=False)\n",
    "\n",
    "df_sim.set_index('Kilometres')['Meter'].plot(title='Meter', kind='line', marker='o', zorder=1, ax=axs[0], grid=True)\n",
    "df_sim.set_index('Kilometres')['Paid (incl. tips)'].plot(title='Paid (incl. tips)', kind='line', marker='o', linestyle='--', ax=axs[1], grid=True)\n",
    "\n",
    "axs[0].set_xlabel('Kilometres')\n",
    "axs[1].set_xlabel('Kilometres')\n",
    "\n",
    "axs[0].set_ylabel('Amount ($)')\n",
    "axs[1].set_ylabel('Amount ($)')\n",
    "\n",
    "axs[0].set_xlim((-0.1,5.1))\n",
    "axs[0].set_ylim((-0.1,20.5))\n",
    "\n",
    "axs[1].set_xlim((-0.1,5.1))\n",
    "axs[1].set_ylim((-0.1,20.5))\n",
    "\n",
    "# axs[0].plot([0,1], [5,7.5], linestyle='--', color='k', zorder=0, alpha=0.6)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_taxi = df_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kilometres</th>\n",
       "      <th>Meter</th>\n",
       "      <th>Paid (incl. tips)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>12.5</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Kilometres  Meter  Paid (incl. tips)\n",
       "0           1    7.5                  8\n",
       "1           2   10.0                 10\n",
       "2           3   12.5                 14\n",
       "3           4   15.0                 15\n",
       "4           5   17.5                 20"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_taxi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(df_taxi[['Kilometres']], df_taxi['Paid (incl. tips)'])\n",
    "\n",
    "df_taxi['Paid (Predicted)'] = reg.predict(df_taxi[['Kilometres']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount Paid = 4.7 + 2.9 * Distance\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    'Amount Paid = {:.1f} + {:.1f} * Distance'.format(\n",
    "        reg.intercept_, reg.coef_[0], \n",
    "    )\n",
    ") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Regressed Line \n",
    "\n",
    "In higher dimensions, the line will be a hyperplane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:28: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 8), sharex=False, sharey=False)\n",
    "\n",
    "df_taxi.tail(5).plot(\n",
    "    label='actual', kind='scatter', x='Kilometres', y='Paid (incl. tips)',\n",
    "    marker='o', alpha=0.95, ax=ax\n",
    ")\n",
    "\n",
    "df_taxi.tail(5).set_index('Kilometres')['Paid (Predicted)'].plot(\n",
    "    title='Paid (incl. tips)', label='estimated', kind='line', \n",
    "    marker='o', linestyle='--', color='k', alpha=0.95, ax=ax\n",
    ")\n",
    "\n",
    "x_min, m_max = 0.8, 5.2\n",
    "y_min, y_max = 5, 22\n",
    "\n",
    "ax.set_xlim((x_min, m_max))\n",
    "ax.set_ylim((y_min, y_max))\n",
    "\n",
    "for x, y1, y2 in df_taxi.tail(5)[\n",
    "    ['Kilometres', 'Paid (incl. tips)', 'Paid (Predicted)']\n",
    "].values.tolist():\n",
    "    ymin, ymax = min(y1, y2), max(y1, y2)\n",
    "    ymin, ymax = (ymin-y_min)/(y_max-y_min) , (ymax-y_min)/(y_max-y_min)\n",
    "    ax.axvline(x=x, ymin=ymin, ymax=ymax, color='k')\n",
    "\n",
    "ax.legend(loc='lower right', fontsize='xx-large')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:33: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(16, 5), sharex=False, sharey=False)\n",
    "\n",
    "df_taxi.set_index('Kilometres')['Meter'].plot(\n",
    "    title='Meter', kind='line', \n",
    "    marker='o', alpha=0.75, ax=axs[0]\n",
    ")\n",
    "df_taxi.set_index('Kilometres')['Paid (incl. tips)'].plot(\n",
    "    label='actual', kind='line', \n",
    "    marker='o', linestyle='--', alpha=0.75, ax=axs[1]\n",
    ")\n",
    "\n",
    "df_taxi.set_index('Kilometres')['Paid (Predicted)'].plot(\n",
    "    title='Paid (incl. tips)', label='estimated', kind='line', \n",
    "    marker='o', linestyle='-.', color='k', alpha=0.75, ax=axs[1]\n",
    ")\n",
    "\n",
    "axs[0].set_xlabel('Kilometres')\n",
    "axs[1].set_xlabel('Kilometres')\n",
    "\n",
    "axs[0].set_ylabel('Amount ($)')\n",
    "axs[1].set_ylabel('Amount ($)')\n",
    "\n",
    "axs[0].set_xlim((-0.1,5.1))\n",
    "axs[0].set_ylim((-0.1,20.5))\n",
    "\n",
    "axs[1].set_xlim((-0.1,5.1))\n",
    "axs[1].set_ylim((-0.1,20.5))\n",
    "\n",
    "axs[1].legend(\n",
    "    markerscale=0.01, loc='lower right', fontsize='xx-large'\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount Paid =  0.0 + 0.0 * Distance\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    'Amount Paid =  {:.1f} + {:.1f} * Distance'.format(\n",
    "        reg.intercept_, reg.coef_[0], \n",
    "    )\n",
    ") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anscombe Data (not in the book)\n",
    "\n",
    "This is an interesting dataset, all 3 y's have the same means and standard deviation, yet they require different regression models to fit each of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_anscombe = pd.DataFrame(\n",
    "    [[10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5],\n",
    "     [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68],\n",
    "     [9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74],\n",
    "     [7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73]]\n",
    ").T\n",
    "\n",
    "df_anscombe.columns = ['x', 'y1', 'y2', 'y3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "      <th>y3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>8.04</td>\n",
       "      <td>9.14</td>\n",
       "      <td>7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>6.95</td>\n",
       "      <td>8.14</td>\n",
       "      <td>6.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>7.58</td>\n",
       "      <td>8.74</td>\n",
       "      <td>12.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>8.81</td>\n",
       "      <td>8.77</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>8.33</td>\n",
       "      <td>9.26</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>9.96</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>7.24</td>\n",
       "      <td>6.13</td>\n",
       "      <td>6.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>4.26</td>\n",
       "      <td>3.10</td>\n",
       "      <td>5.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>10.84</td>\n",
       "      <td>9.13</td>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>4.82</td>\n",
       "      <td>7.26</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>5.68</td>\n",
       "      <td>4.74</td>\n",
       "      <td>5.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         y1    y2     y3\n",
       "x                       \n",
       "10.0   8.04  9.14   7.46\n",
       "8.0    6.95  8.14   6.77\n",
       "13.0   7.58  8.74  12.74\n",
       "9.0    8.81  8.77   7.11\n",
       "11.0   8.33  9.26   7.81\n",
       "14.0   9.96  8.10   8.84\n",
       "6.0    7.24  6.13   6.08\n",
       "4.0    4.26  3.10   5.39\n",
       "12.0  10.84  9.13   8.15\n",
       "7.0    4.82  7.26   6.42\n",
       "5.0    5.68  4.74   5.73"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_anscombe.set_index('x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "      <th>y3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.500909</td>\n",
       "      <td>7.500909</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.031568</td>\n",
       "      <td>2.031657</td>\n",
       "      <td>2.030424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            y1        y2        y3\n",
       "mean  7.500909  7.500909  7.500000\n",
       "std   2.031568  2.031657  2.030424"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_anscombe[['y1', 'y2', 'y3']].apply([np.mean, np.std])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "image/png": 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wO/L4hXV7wwAAYACc98MwWclAa+wNAwAA6BYhA62xNwwAAKBbhAy0Zntv2IHZS/aGAQAAdIAzGWiN23ICADDJzqxt5OjJ1Tx5fjZ3/UU/K8vzOTi3t+2yYFesZAAAAGjB9kHoj1+4I6f6lw9Ch3EnZAAAAGiBg9DpIiEDAABACxyEThcJGQAAAFrgIHS6yMGPAAAALXAQOl1kJQMAAADQCCEDAAAA0AjbJQAAALacWdvI0ZOrOXtxM/tnprKyPJ+Dc3vbLgvGhpUMAAAAW46eXM2p/npOr23mVH89R06stl0SjBUhAwAAwJazFzd3vAZ2JmQAAADYsn9masdrYGdCBgAAgC0ry/NZWpjOPXdOZWlhOivL822XBGPFwY8AAABbDs7tzfHDC22XAWPLSgYAAACgEUIGAAAAoBFCBgB4DmfWNnLoWD/3PzybQ8f6eez8RtslAQCMJCEDADyH7XumP37hDvdMB2iBsBfGR+shQyllvpTyYCnl66WUR0spP952TQBwNfdMB2iXsBfGR+shQ5IPJPmjWuuPJnlFkkdbrgcAruGe6QDtEvbC+Gg1ZCil7EvyU0k+kiS11vVaq1gSgJGyfc/0A7OX3DMdoAXCXhgfpdba3ouX8sokH0ry57m8iuFPk7y91vrtJDl37tyV4nq9Xis1AlxvcXHxyuN9+/aVFksZOHMYGEXm8OT51t+W/NY3pvM3GyXze2v+7d9bz0ue197/x8Ck22kOtx0yvCrJF5L8ZK31i6WUDyRZq7X+m+TaoXqrer3eNY13RRf76mJPSTf76mJPye76mqQfbm9VFz9futhT0s2+uthT0s2+dtuTOXxjPl/GRxf76mJPSTf7anoOt30mwxNJnqi1fnHr+sEk97VYDwAAAHCbWg0Zaq1PJXm8lPKyradem8tbJwAAAIAxs6ftApL8apJPlFKmk5xO8sst1wMAAADchtZDhlrrl5O8qu06AACAm3dmbSNHT67m7MXN7J+ZysryfA7O7W27LKBlbZ/JMNbOrG3k0LF+7vvUUzl0rJ/Hzm+0XRIAAAzF0ZOrOdVfz+m1zZzqr+fICXeiB4QMu2KwAgAwqc5e3NzxGphMQoZdMFgBAJhU+2emdrwGJpOQYRcMVgAAJtXK8nyWFqZzz51TWVqYzsryfNslASOg9YMfx9nK8nyOnLj2sBsAAJgEB+f25vjhhbbLAEaMkGEXDFYAAAD4HtslAAAAgEYIGQAAAIBGCBkAAKBDzqxt5NCxfu5/eDaHjvXz2PmNtksCJoiQAQAAOuToydWc6q/n8Qt35FR/PUdOrLZdEjBBhAwAANAhZy9u7ngNMEhCBgAA6JD9M1M7XgMMkpABAAA6ZGV5PksL0zkweylLC9NZWZ5vuyRgguxpuwAAAKA5B+f25vjhhfR6vSwuHmi7HGDCWMkAAAAANELIAAAAADRCyAAAAAA0QsgAAAAANELIAAAAADRCyAAAAAA0QsgAAAAANELIAAAAADRiT9sFMFrOrG3k6MnVnL24mf0zU1lZns/Bub1tlwUAAMAYsJKBaxw9uZpT/fWcXtvMqf56jpxYbbskAAAAxoSVDFzj7MXNHa8BRonVVwAAo8VKBq6xf2Zqx2uAUWL1FQDAaBEycI2V5fksLUznnjunsrQwnZXl+bZLArghq68AAEaL7RJc4+Dc3hw/vNB2GQA3Zf/MVE5n85prAADaYyUDAGPL6isAgNFiJQMAY8vqKwCA0WIlAwAAANAIIQMAAADQCCEDAAAA0AghAwAAANAIIQMAAADQCCEDAAAA0IjOhQxn1jZy6Fg/9z88m0PH+nns/EbbJQEAAMBE6FzIcPTkak711/P4hTtyqr+eIydW2y4JAAAAJkLnQoazFzd3vAYAAAAGo3Mhw/6ZqR2vAQAAgMHoXMiwsjyfpYXpHJi9lKWF6awsz7ddEgAAAEyEPW0XkCSllKkkDyf5Vq318G4+1sG5vTl+eCG9Xi+LiweaKRAAAAB4TqOykuHtSR5tuwgAAADg9rUeMpRS7k7yM0k+3HYtAAAAwO1rPWRI8v4k70xyqe1CAAAAgNtXaq3tvXgph5P801rrr5RSXpPkN64+k+HcuXNXiuv1ei1UCPD9FhcXrzzet29fabGUgTOHgVFkDgO0a6c53HbI8NtJ/nmS7yaZTXJnkk/XWt+UXDtUb9Xlgx8Xn/svjpku9tXFnpJu9tXFnpLd9TVJP9zeqi5+vnSxp6SbfXWxp6Sbfe22J3P4xny+jI8u9tXFnpJu9tX0HG51u0St9d211rtrrS9N8oYkn98OGAAAAIDxMgpnMgAAAAAdsKftArbVWv84yR+3XAYAAABwm6xkAAAAABohZAAAAAAaIWQAAAAAGiFkAAAAABohZAAAAAAaIWQAAAAAGiFkAAAAABohZAAAAAAaIWQAAAAAGiFkAAAAABohZAAAAAAaIWQAAAAAGiFkAAAAABohZAAAAAAaIWQAAAAAGiFkAKBRZ9Y2cuhYP/c/PJtDx/p57PxG2yUBADAkQgYAGnX05GpO9dfz+IU7cqq/niMnVtsuCQCAIREyANCosxc3d7wGAKC7hAwANGr/zNSO1wAAdJeQAYBGrSzPZ2lhOgdmL2VpYTory/NtlwQAwJDsabsAALrl4NzeHD+8kF6vl8XFA22XAwDAEFnJAAAAADRCyAAAAAA0QsgAAAAANELIAAAAADRCyAAAAAA0QsgAAAAANELIAAAAADRCyAAAAAA0QsgAAAAANKLUWtuu4YbOnTs3usUBJNm3b19pu4ZBMoeBUWcOA7Tr+jlsJQMAAADQCCEDAAAA0IiR3i4BAAAAjA8rGQAAAIBGdDJkKKVMlVL+bynlWNu1NKWUMl9KebCU8vVSyqOllB9vu6YmlFLeUUr5s1LK10opnyylzLZd060qpXy0lPJ0KeVrVz33g6WUh0opva0/X9hmjbfjBn39ztbn4FdLKZ8ppcy3WeOteraernrbr5dSaillfxu1dY05PB66MIMTc7jNGm+VOTw85vB4MIdHVxdncDKcOdzJkCHJ25M82nYRDftAkj+qtf5oklekA/2VUl6S5G1JXlVrvTfJVJI3tFvVbXkgyeuue+5dST5Xa11M8rmt63HzQL6/r4eS3FtrfXmSbyR597CL2qUH8v09pZRyIMmhJN8cdkEdZg6PuA7N4MQcHicPxBweFnN4xJnDI++BdG8GJ0OYw50LGUopdyf5mSQfbruWppRS9iX5qSQfSZJa63qtdbXdqhqzJ8nzSil7kjw/yV+1XM8tq7WeTPLMdU+/PsnHth5/LMnPDbWoBjxbX7XW47XW725dfiHJ3UMvbBdu8N8qSX43yTuTOKSmAebwWBn7GZyYw0MvbBfM4eEwh8eKOTyiujiDk+HM4c6FDEnen8v/OJfaLqRBP5ykn+Q/by17+3Ap5QfaLmq3aq3fSvK+XE7LnkxyrtZ6vN2qGvOiWuuTW4+fSvKiNosZkLck+cO2i9itUsrrk3yr1vqVtmvpEHN4DHR8Bifm8NgwhwfCHB4D5vDY68QMTpqfw50KGUoph5M8XWv907ZradieJPcl+U+11r+f5NsZv+VG32drX9brc/mbxouT/EAp5U3tVtW8evkWLp36zUwp5TeTfDfJJ9quZTdKKc9P8q+T/FbbtXSFOTw+JmUGJ+bwKDOHm2cOjw9zeHx1ZQYng5nDnQoZkvxkkp8tpZxJ8l+T/MNSysfbLakRTyR5otb6xa3rB3N5yI67n07yl7XWfq11I8mnk/xEyzU15a9LKXclydafT7dcT2NKKb+U5HCSX6zjfw/cH8nlb+xf2Zobdyf5Uinlh1qtaryZw+OjyzM4MYfHhTncPHN4fJjDY6hjMzgZwBzuVMhQa313rfXuWutLc/nQlM/XWsc+Day1PpXk8VLKy7aeem2SP2+xpKZ8M8mrSynPL6WUXO5rrA/wucpnk7x56/Gbk/xBi7U0ppTyulxefvmztdbvtF3PbtVaH6m1/p1a60u35sYTSe7b+prjNpjDY6XLMzgxh8eCOdw8c3ismMNjpmszOBnMHO5UyNBxv5rkE6WUryZ5ZZJ/13I9u7aVRD+Y5EtJHsnlz8cPtVrUbSilfDLJnyR5WSnliVLKW5O8N8k/KqX0cjmlfm+bNd6OG/T1e0nmkjxUSvlyKeWDrRZ5i27QE9ysTs3hrszgxBxutchbZA6zS+bwiOriHO7iDE6GM4dLN1Z4AAAAAG2zkgEAAABohJABAAAAaISQAQAAAGiEkAEAAABohJABAAAAaISQAQAAAGiEkAEAAABohJABAAAAaISQgc4rpfxIKeWZUsp9W9cvLqX0Symvabk0gM4zgwHaZQ4zbKXW2nYNMHCllCNJ3pHkVUk+k+SRWutvtFsVwGQwgwHaZQ4zTEIGJkYp5bNJfjhJTfIPaq0XWy4JYGKYwQDtMocZFtslmCQrSe5N8u8NVYChM4MB2mUOMxRWMjARSikvSPKVJP8nyT9J8mO11mfarQpgMpjBAO0yhxkmIQMToZTykSQvqLX+QinlQ0nma60/33ZdAJPADAZolznMMNkuQeeVUl6f5HVJ/uXWU7+W5L5Syi+2VxXAZDCDAdplDjNsVjIAAAAAjbCSAQAAAGiEkAEAAABohJABAAAAaISQAQAAAGiEkAEAAABohJABAAAAaISQAQAAAGiEkAEAAABohJABAAAAaMT/B7iY8JK2PxMTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(16, 5), sharey=True)\n",
    "\n",
    "for i in range(3):\n",
    "\n",
    "    df_anscombe.plot(\n",
    "        kind='scatter', x='x', y=f'y{i+1}', ax=axs[i]\n",
    "    )\n",
    "    \n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.67, MSE: 1.25, MAE: 0.84,  MdAE: 0.74   @ target=y1\n",
      "R2: 0.67, MSE: 1.25, MAE: 0.97,  MdAE: 0.76   @ target=y2\n",
      "R2: 0.67, MSE: 1.25, MAE: 0.72,  MdAE: 0.39   @ target=y3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import (\n",
    "    mean_squared_error, mean_absolute_error, median_absolute_error, r2_score\n",
    ")\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(16, 5), sharey=False)\n",
    "\n",
    "for i, target in enumerate(['y1', 'y2', 'y3']):\n",
    "\n",
    "    reg = LinearRegression()\n",
    "    reg.fit(df_anscombe[['x']], df_anscombe[target])\n",
    "    df_anscombe[f'{target}_pred'] = reg.predict(df_anscombe[['x']])\n",
    "    \n",
    "    df_anscombe.plot(\n",
    "        title=f'{target} vs predicted {target}', kind='scatter', x='x', y=target, ax=axs[i], marker='o', ylim=(0,15), alpha=0.5\n",
    "    )\n",
    "    \n",
    "    # df_anscombe.set_index('x')[f'{target}_pred'].plot(kind='line', ax=axs[i], color='r')\n",
    "    df_anscombe.plot(\n",
    "        kind='scatter', x='x', y=f'{target}_pred', ax=axs[i], marker='*', color='k'\n",
    "    )\n",
    "\n",
    "    \n",
    "    print(\n",
    "        'R2: {:.2f}, MSE: {:.2f}, MAE: {:.2f},  MdAE: {:.2f}   @ target={}'.format(\n",
    "            r2_score(df_anscombe[target], df_anscombe[f'{target}_pred']),\n",
    "            mean_squared_error(df_anscombe[target], df_anscombe[f'{target}_pred']),\n",
    "            mean_absolute_error(df_anscombe[target], df_anscombe[f'{target}_pred']),\n",
    "            median_absolute_error(df_anscombe[target], df_anscombe[f'{target}_pred']),\n",
    "            target\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1.31, MAE: 1.01 @ target=y1\n",
      "MSE: 0.72, MAE: 0.68 @ target=y2\n",
      "MSE: 1.34, MAE: 0.98 @ target=y3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import export_text\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(16, 5), sharey=False)\n",
    "\n",
    "for i, target in enumerate(['y1', 'y2', 'y3']):\n",
    "\n",
    "    reg = DecisionTreeRegressor(max_depth=1)\n",
    "    reg.fit(df_anscombe[['x']], df_anscombe[target])\n",
    "    df_anscombe[f'{target}_pred'] = reg.predict(df_anscombe[['x']])\n",
    "    \n",
    "    df_anscombe.plot(\n",
    "        kind='scatter', x='x', y=target, ax=axs[i], marker='o', ylim=(0,15), alpha=0.5\n",
    "    )\n",
    "    \n",
    "    # df_anscombe.set_index('x')[f'{target}_pred'].plot(kind='line', ax=axs[i], color='r')\n",
    "    df_anscombe.plot(\n",
    "        kind='scatter', x='x', y=f'{target}_pred', ax=axs[i], marker='*', color='k'\n",
    "    )\n",
    "    \n",
    "    print(\n",
    "        'MSE: {:.2f}, MAE: {:.2f} @ target={}'.format(\n",
    "            mean_squared_error(df_anscombe[target], df_anscombe[f'{target}_pred']),\n",
    "            mean_absolute_error(df_anscombe[target], df_anscombe[f'{target}_pred']),\n",
    "            target\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- x <= 12.5\n",
      "|   |--- value: [6.8]\n",
      "|--- x >  12.5\n",
      "|   |--- value: [10.8]\n",
      "\n"
     ]
    }
   ],
   "source": [
    " print(export_text(reg, feature_names=['x'], spacing=3, decimals=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boston Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "\n",
    "boston = load_boston()\n",
    "\n",
    "df_dataset = pd.DataFrame(\n",
    "    boston.data,\n",
    "    columns=boston.feature_names,\n",
    ")\n",
    "\n",
    "df_dataset['target'] = boston.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next code allows me to switch between 3 different datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston, fetch_california_housing, load_diabetes\n",
    "\n",
    "dataset_selection = ['boston', 'california', 'diabetes'][0]\n",
    "\n",
    "if dataset_selection == 'diabetes':\n",
    "    _dataset = load_diabetes()\n",
    "elif dataset_selection == 'boston':\n",
    "    _dataset = load_boston()\n",
    "else:\n",
    "    _dataset = fetch_california_housing()\n",
    "\n",
    "\n",
    "df_dataset = pd.DataFrame(\n",
    "    _dataset.data,\n",
    "    columns=_dataset.feature_names,\n",
    ")\n",
    "\n",
    "df_dataset['target'] = _dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(_dataset.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  target  \n",
       "0     15.3  396.90   4.98    24.0  \n",
       "1     17.8  396.90   9.14    21.6  \n",
       "2     17.8  392.83   4.03    34.7  \n",
       "3     18.7  394.63   2.94    33.4  \n",
       "4     18.7  396.90   5.33    36.2  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM         3.613524\n",
       "ZN          11.363636\n",
       "INDUS       11.136779\n",
       "CHAS         0.069170\n",
       "NOX          0.554695\n",
       "RM           6.284634\n",
       "AGE         68.574901\n",
       "DIS          3.795043\n",
       "RAD          9.549407\n",
       "TAX        408.237154\n",
       "PTRATIO     18.455534\n",
       "B          356.674032\n",
       "LSTAT       12.653063\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dataset[boston.feature_names].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if we have null values \n",
    "\n",
    "df_dataset.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    506.000000\n",
       "mean      22.532806\n",
       "std        9.197104\n",
       "min        5.000000\n",
       "50%       21.200000\n",
       "90%       34.800000\n",
       "95%       43.400000\n",
       "99%       50.000000\n",
       "max       50.000000\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the target's distribution\n",
    "\n",
    "df_dataset['target'].describe(percentiles=[.9, .95, .99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>506.0</td>\n",
       "      <td>3.613524</td>\n",
       "      <td>8.601545</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.25651</td>\n",
       "      <td>3.677083</td>\n",
       "      <td>88.9762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>506.0</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>506.0</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.46000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>9.69000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>27.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>506.0</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>506.0</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.38500</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>0.53800</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>0.8710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>506.0</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>3.56100</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>6.20850</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>8.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>506.0</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.90000</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>77.50000</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>506.0</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>1.12960</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>3.20745</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>12.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>506.0</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>506.0</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>187.00000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>330.00000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>711.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>506.0</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>19.05000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>22.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>506.0</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>0.32000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>391.44000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>396.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>506.0</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>1.73000</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>11.36000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>37.9700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count        mean         std        min         25%        50%  \\\n",
       "CRIM     506.0    3.613524    8.601545    0.00632    0.082045    0.25651   \n",
       "ZN       506.0   11.363636   23.322453    0.00000    0.000000    0.00000   \n",
       "INDUS    506.0   11.136779    6.860353    0.46000    5.190000    9.69000   \n",
       "CHAS     506.0    0.069170    0.253994    0.00000    0.000000    0.00000   \n",
       "NOX      506.0    0.554695    0.115878    0.38500    0.449000    0.53800   \n",
       "RM       506.0    6.284634    0.702617    3.56100    5.885500    6.20850   \n",
       "AGE      506.0   68.574901   28.148861    2.90000   45.025000   77.50000   \n",
       "DIS      506.0    3.795043    2.105710    1.12960    2.100175    3.20745   \n",
       "RAD      506.0    9.549407    8.707259    1.00000    4.000000    5.00000   \n",
       "TAX      506.0  408.237154  168.537116  187.00000  279.000000  330.00000   \n",
       "PTRATIO  506.0   18.455534    2.164946   12.60000   17.400000   19.05000   \n",
       "B        506.0  356.674032   91.294864    0.32000  375.377500  391.44000   \n",
       "LSTAT    506.0   12.653063    7.141062    1.73000    6.950000   11.36000   \n",
       "\n",
       "                75%       max  \n",
       "CRIM       3.677083   88.9762  \n",
       "ZN        12.500000  100.0000  \n",
       "INDUS     18.100000   27.7400  \n",
       "CHAS       0.000000    1.0000  \n",
       "NOX        0.624000    0.8710  \n",
       "RM         6.623500    8.7800  \n",
       "AGE       94.075000  100.0000  \n",
       "DIS        5.188425   12.1265  \n",
       "RAD       24.000000   24.0000  \n",
       "TAX      666.000000  711.0000  \n",
       "PTRATIO   20.200000   22.0000  \n",
       "B        396.225000  396.9000  \n",
       "LSTAT     16.955000   37.9700  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Understanding the loaded features \n",
    "\n",
    "df_dataset[boston.feature_names].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:8: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(16, 8))\n",
    "\n",
    "df_dataset['target'].plot(title='Distribution of target prices', kind='hist', ax=axs[0])\n",
    "\n",
    "df_dataset[boston.feature_names].mean().plot(title='Mean of features', kind='bar', ax=axs[1])\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns \n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(16, 8))\n",
    "\n",
    "sns.heatmap(data=df_dataset.corr().abs().round(2), annot=True, cmap=\"YlGnBu\", center=0.5, ax=ax)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train, df_test = train_test_split(df_dataset, test_size=0.4)\n",
    "\n",
    "x_train = df_train[_dataset.feature_names]\n",
    "x_test  = df_test[_dataset.feature_names]\n",
    "\n",
    "y_train = df_train['target']\n",
    "y_test  = df_test['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22.532806324110677, 22.21023102310231, 23.014285714285716)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dataset['target'].mean(), df_train['target'].mean(), df_test['target'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21.2, 21.1, 21.6)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dataset['target'].median(), df_train['target'].median(), df_test['target'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyRegressor\n",
    "\n",
    "baselin = DummyRegressor(strategy='mean')\n",
    "baselin.fit(x_train, y_train)\n",
    "\n",
    "y_test_baselin = baselin.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.21023102, 22.21023102, 22.21023102, 22.21023102, 22.21023102])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_baselin[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(x_train, y_train)\n",
    "\n",
    "y_test_pred = reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing our Linear Regression to the baseline model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Regressor = 0.71 vs Baseline = -0.01\n",
      "MAE Regressor = 3.88 vs Baseline = 6.89\n",
      "MSE Regressor = 28.83 vs Baseline = 99.15\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(\n",
    "    'R2 Regressor = {:.2f} vs Baseline = {:.2f}'.format(\n",
    "        r2_score(y_test, y_test_pred), \n",
    "        r2_score(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    'MAE Regressor = {:.2f} vs Baseline = {:.2f}'.format(\n",
    "        mean_absolute_error(y_test, y_test_pred), \n",
    "        mean_absolute_error(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\n",
    "    'MSE Regressor = {:.2f} vs Baseline = {:.2f}'.format(\n",
    "        mean_squared_error(y_test, y_test_pred), \n",
    "        mean_squared_error(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coeff</th>\n",
       "      <th>ABS(Coeff)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Features</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>3.523780</td>\n",
       "      <td>3.523780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>2.138745</td>\n",
       "      <td>2.138745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.269508</td>\n",
       "      <td>0.269508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>0.055773</td>\n",
       "      <td>0.055773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.012082</td>\n",
       "      <td>0.012082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.003247</td>\n",
       "      <td>0.003247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>-0.010670</td>\n",
       "      <td>0.010670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>-0.013802</td>\n",
       "      <td>0.013802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>-0.018162</td>\n",
       "      <td>0.018162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>-0.641409</td>\n",
       "      <td>0.641409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>-0.848058</td>\n",
       "      <td>0.848058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-1.440938</td>\n",
       "      <td>1.440938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>-15.764323</td>\n",
       "      <td>15.764323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Coeff  ABS(Coeff)\n",
       "Features                       \n",
       "CHAS       3.523780    3.523780\n",
       "RM         2.138745    2.138745\n",
       "RAD        0.269508    0.269508\n",
       "ZN         0.055773    0.055773\n",
       "B          0.012082    0.012082\n",
       "AGE        0.003247    0.003247\n",
       "TAX       -0.010670    0.010670\n",
       "CRIM      -0.013802    0.013802\n",
       "INDUS     -0.018162    0.018162\n",
       "LSTAT     -0.641409    0.641409\n",
       "PTRATIO   -0.848058    0.848058\n",
       "DIS       -1.440938    1.440938\n",
       "NOX      -15.764323   15.764323"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feature_importance = pd.DataFrame(\n",
    "    {\n",
    "        'Features': x_train.columns,\n",
    "        'Coeff': reg.coef_,\n",
    "        'ABS(Coeff)': abs(reg.coef_),\n",
    "    }\n",
    ").set_index('Features').sort_values('Coeff', ascending=False)\n",
    "\n",
    "df_feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:16: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "important_featurs = df_feature_importance.sort_values('ABS(Coeff)', ascending=False).index.tolist()[:6]\n",
    "\n",
    "fig, axs = plt.subplots(1, len(important_featurs), figsize=(16, 5), sharey=True)\n",
    "\n",
    "fig.suptitle(\"Featurs vs Target\", fontsize=14)\n",
    "\n",
    "for i, feature in enumerate(important_featurs):\n",
    "\n",
    "    df_dataset.plot(\n",
    "        kind='scatter',\n",
    "        x=feature,\n",
    "        y='target',\n",
    "        ax=axs[i]\n",
    "    )\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "reg = LinearRegression()\n",
    "\n",
    "scaler.fit(x_train) \n",
    "x_train_scaled = scaler.transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)\n",
    "\n",
    "reg.fit(x_train_scaled, y_train)\n",
    "\n",
    "y_test_pred = reg.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Regressor = 0.71 vs Baseline = -0.01\n",
      "MAE Regressor = 3.88 vs Baseline = 6.89\n",
      "MSE Regressor = 28.83 vs Baseline = 99.15\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(\n",
    "    'R2 Regressor = {:.2f} vs Baseline = {:.2f}'.format(\n",
    "        r2_score(y_test, y_test_pred), \n",
    "        r2_score(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    'MAE Regressor = {:.2f} vs Baseline = {:.2f}'.format(\n",
    "        mean_absolute_error(y_test, y_test_pred), \n",
    "        mean_absolute_error(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\n",
    "    'MSE Regressor = {:.2f} vs Baseline = {:.2f}'.format(\n",
    "        mean_squared_error(y_test, y_test_pred), \n",
    "        mean_squared_error(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coeff</th>\n",
       "      <th>ABS(Coeff)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Features</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>11.162109</td>\n",
       "      <td>11.162109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>6.198673</td>\n",
       "      <td>6.198673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>5.577348</td>\n",
       "      <td>5.577348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4.791642</td>\n",
       "      <td>4.791642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>3.523780</td>\n",
       "      <td>3.523780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.315264</td>\n",
       "      <td>0.315264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>-0.495453</td>\n",
       "      <td>0.495453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>-1.014823</td>\n",
       "      <td>1.014823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>-5.580194</td>\n",
       "      <td>5.580194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>-7.661461</td>\n",
       "      <td>7.661461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>-7.971741</td>\n",
       "      <td>7.971741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-13.805197</td>\n",
       "      <td>13.805197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>-23.122777</td>\n",
       "      <td>23.122777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Coeff  ABS(Coeff)\n",
       "Features                       \n",
       "RM        11.162109   11.162109\n",
       "RAD        6.198673    6.198673\n",
       "ZN         5.577348    5.577348\n",
       "B          4.791642    4.791642\n",
       "CHAS       3.523780    3.523780\n",
       "AGE        0.315264    0.315264\n",
       "INDUS     -0.495453    0.495453\n",
       "CRIM      -1.014823    1.014823\n",
       "TAX       -5.580194    5.580194\n",
       "NOX       -7.661461    7.661461\n",
       "PTRATIO   -7.971741    7.971741\n",
       "DIS      -13.805197   13.805197\n",
       "LSTAT    -23.122777   23.122777"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feature_importance = pd.DataFrame(\n",
    "    {\n",
    "        'Features': x_train.columns,\n",
    "        'Coeff': reg.coef_,\n",
    "        'ABS(Coeff)': abs(reg.coef_),\n",
    "    }\n",
    ").set_index('Features').sort_values('Coeff', ascending=False)\n",
    "\n",
    "df_feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:16: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "important_featurs = df_feature_importance.sort_values('ABS(Coeff)', ascending=False).index.tolist()[:5]\n",
    "\n",
    "fig, axs = plt.subplots(1, len(important_featurs), figsize=(16, 5), sharey=True)\n",
    "\n",
    "fig.suptitle(\"Featurs vs Target\", fontsize=14)\n",
    "\n",
    "for i, feature in enumerate(important_featurs):\n",
    "\n",
    "    df_dataset.plot(\n",
    "        kind='scatter',\n",
    "        x=feature,\n",
    "        y='target',\n",
    "        ax=axs[i]\n",
    "    )\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Polynomial Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "poly = PolynomialFeatures(degree=3)\n",
    "x_train_poly = poly.fit_transform(x_train)\n",
    "x_test_poly = poly.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_translator = [(f'x{i}', feature) for i, feature in enumerate(x_train.columns, 0)]\n",
    "\n",
    "def translate_feature_names(s):\n",
    "    for key, val in reversed(feature_translator):\n",
    "        s = s.replace(key, val)\n",
    "    return s\n",
    "\n",
    "poly_features = [\n",
    "    translate_feature_names(f) for f in poly.get_feature_names()\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1', 'CRIM', 'ZN', 'INDUS', 'CHAS']"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_features[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_poly = pd.DataFrame(x_train_poly, columns=poly_features)\n",
    "x_test_poly = pd.DataFrame(x_test_poly, columns=poly_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "reg = LinearRegression(fit_intercept=False)\n",
    "reg.fit(x_train_poly, y_train)\n",
    "\n",
    "y_test_pred = reg.predict(x_test_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 560)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_poly.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Regressor = -1779.801 vs Baseline = -0.0\n",
      "MAE Regressor = 83.901 vs Baseline = 6.9\n",
      "MSE Regressor = 175416.176 vs Baseline = 99.2\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(\n",
    "    'R2 Regressor = {:.3f} vs Baseline = {:.1f}'.format(\n",
    "        r2_score(y_test, y_test_pred), \n",
    "        r2_score(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    'MAE Regressor = {:.3f} vs Baseline = {:.1f}'.format(\n",
    "        mean_absolute_error(y_test, y_test_pred), \n",
    "        mean_absolute_error(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\n",
    "    'MSE Regressor = {:.3f} vs Baseline = {:.1f}'.format(\n",
    "        mean_squared_error(y_test, y_test_pred), \n",
    "        mean_squared_error(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>...</th>\n",
       "      <th>ZN0^3</th>\n",
       "      <th>ZN0^2 ZN1</th>\n",
       "      <th>ZN0^2 ZN2</th>\n",
       "      <th>ZN0 ZN1^2</th>\n",
       "      <th>ZN0 ZN1 ZN2</th>\n",
       "      <th>ZN0 ZN2^2</th>\n",
       "      <th>ZN1^3</th>\n",
       "      <th>ZN1^2 ZN2</th>\n",
       "      <th>ZN1 ZN2^2</th>\n",
       "      <th>ZN2^3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.223404</td>\n",
       "      <td>0.415626</td>\n",
       "      <td>-0.067407</td>\n",
       "      <td>0.438434</td>\n",
       "      <td>-0.264047</td>\n",
       "      <td>0.398427</td>\n",
       "      <td>-0.409130</td>\n",
       "      <td>0.658170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.323198</td>\n",
       "      <td>-0.289927</td>\n",
       "      <td>0.515537</td>\n",
       "      <td>-0.445567</td>\n",
       "      <td>0.101581</td>\n",
       "      <td>0.514396</td>\n",
       "      <td>-0.529322</td>\n",
       "      <td>-0.016093</td>\n",
       "      <td>0.214623</td>\n",
       "      <td>0.477870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.223404</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.546942</td>\n",
       "      <td>-0.076711</td>\n",
       "      <td>-0.526713</td>\n",
       "      <td>0.328753</td>\n",
       "      <td>-0.569498</td>\n",
       "      <td>0.670739</td>\n",
       "      <td>-0.332850</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.350266</td>\n",
       "      <td>-0.091572</td>\n",
       "      <td>-0.429276</td>\n",
       "      <td>0.061411</td>\n",
       "      <td>-0.349521</td>\n",
       "      <td>-0.336015</td>\n",
       "      <td>0.209895</td>\n",
       "      <td>-0.293432</td>\n",
       "      <td>-0.304253</td>\n",
       "      <td>-0.253397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.415626</td>\n",
       "      <td>-0.546942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045772</td>\n",
       "      <td>0.757920</td>\n",
       "      <td>-0.407121</td>\n",
       "      <td>0.666270</td>\n",
       "      <td>-0.729739</td>\n",
       "      <td>0.572268</td>\n",
       "      <td>...</td>\n",
       "      <td>0.407733</td>\n",
       "      <td>-0.012417</td>\n",
       "      <td>0.624719</td>\n",
       "      <td>-0.224279</td>\n",
       "      <td>0.446052</td>\n",
       "      <td>0.547373</td>\n",
       "      <td>-0.399147</td>\n",
       "      <td>0.342261</td>\n",
       "      <td>0.462458</td>\n",
       "      <td>0.448283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.067407</td>\n",
       "      <td>-0.076711</td>\n",
       "      <td>0.045772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.071084</td>\n",
       "      <td>0.126426</td>\n",
       "      <td>0.078795</td>\n",
       "      <td>-0.106944</td>\n",
       "      <td>0.004661</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108271</td>\n",
       "      <td>-0.010076</td>\n",
       "      <td>-0.126720</td>\n",
       "      <td>0.017174</td>\n",
       "      <td>-0.066676</td>\n",
       "      <td>-0.098913</td>\n",
       "      <td>0.031086</td>\n",
       "      <td>-0.041579</td>\n",
       "      <td>-0.056364</td>\n",
       "      <td>-0.080771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN0 ZN2^2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.514396</td>\n",
       "      <td>-0.336015</td>\n",
       "      <td>0.547373</td>\n",
       "      <td>-0.098913</td>\n",
       "      <td>0.538193</td>\n",
       "      <td>-0.530627</td>\n",
       "      <td>0.558034</td>\n",
       "      <td>-0.485305</td>\n",
       "      <td>0.467276</td>\n",
       "      <td>...</td>\n",
       "      <td>0.411844</td>\n",
       "      <td>-0.052867</td>\n",
       "      <td>0.936672</td>\n",
       "      <td>-0.244088</td>\n",
       "      <td>0.659143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.383285</td>\n",
       "      <td>0.533159</td>\n",
       "      <td>0.807586</td>\n",
       "      <td>0.966147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN1^3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.529322</td>\n",
       "      <td>0.209895</td>\n",
       "      <td>-0.399147</td>\n",
       "      <td>0.031086</td>\n",
       "      <td>-0.440796</td>\n",
       "      <td>0.162205</td>\n",
       "      <td>-0.342146</td>\n",
       "      <td>0.350005</td>\n",
       "      <td>-0.463462</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.187553</td>\n",
       "      <td>0.711408</td>\n",
       "      <td>-0.390007</td>\n",
       "      <td>0.928230</td>\n",
       "      <td>0.251364</td>\n",
       "      <td>-0.383285</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.419015</td>\n",
       "      <td>0.068801</td>\n",
       "      <td>-0.339574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN1^2 ZN2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.016093</td>\n",
       "      <td>-0.293432</td>\n",
       "      <td>0.342261</td>\n",
       "      <td>-0.041579</td>\n",
       "      <td>0.286858</td>\n",
       "      <td>-0.507258</td>\n",
       "      <td>0.409186</td>\n",
       "      <td>-0.284093</td>\n",
       "      <td>0.081219</td>\n",
       "      <td>...</td>\n",
       "      <td>0.258771</td>\n",
       "      <td>0.577637</td>\n",
       "      <td>0.564306</td>\n",
       "      <td>0.535625</td>\n",
       "      <td>0.962848</td>\n",
       "      <td>0.533159</td>\n",
       "      <td>0.419015</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.887052</td>\n",
       "      <td>0.468602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN1 ZN2^2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214623</td>\n",
       "      <td>-0.304253</td>\n",
       "      <td>0.462458</td>\n",
       "      <td>-0.056364</td>\n",
       "      <td>0.439361</td>\n",
       "      <td>-0.561234</td>\n",
       "      <td>0.497390</td>\n",
       "      <td>-0.405439</td>\n",
       "      <td>0.247726</td>\n",
       "      <td>...</td>\n",
       "      <td>0.284099</td>\n",
       "      <td>0.307160</td>\n",
       "      <td>0.754920</td>\n",
       "      <td>0.196187</td>\n",
       "      <td>0.920995</td>\n",
       "      <td>0.807586</td>\n",
       "      <td>0.068801</td>\n",
       "      <td>0.887052</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.780267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN2^3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.477870</td>\n",
       "      <td>-0.253397</td>\n",
       "      <td>0.448283</td>\n",
       "      <td>-0.080771</td>\n",
       "      <td>0.478097</td>\n",
       "      <td>-0.484082</td>\n",
       "      <td>0.451311</td>\n",
       "      <td>-0.412170</td>\n",
       "      <td>0.375762</td>\n",
       "      <td>...</td>\n",
       "      <td>0.268354</td>\n",
       "      <td>-0.109420</td>\n",
       "      <td>0.822817</td>\n",
       "      <td>-0.249773</td>\n",
       "      <td>0.568925</td>\n",
       "      <td>0.966147</td>\n",
       "      <td>-0.339574</td>\n",
       "      <td>0.468602</td>\n",
       "      <td>0.780267</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560 rows × 560 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1      CRIM        ZN     INDUS      CHAS       NOX        RM  \\\n",
       "1         NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "CRIM      NaN  1.000000 -0.223404  0.415626 -0.067407  0.438434 -0.264047   \n",
       "ZN        NaN -0.223404  1.000000 -0.546942 -0.076711 -0.526713  0.328753   \n",
       "INDUS     NaN  0.415626 -0.546942  1.000000  0.045772  0.757920 -0.407121   \n",
       "CHAS      NaN -0.067407 -0.076711  0.045772  1.000000  0.071084  0.126426   \n",
       "...        ..       ...       ...       ...       ...       ...       ...   \n",
       "ZN0 ZN2^2 NaN  0.514396 -0.336015  0.547373 -0.098913  0.538193 -0.530627   \n",
       "ZN1^3     NaN -0.529322  0.209895 -0.399147  0.031086 -0.440796  0.162205   \n",
       "ZN1^2 ZN2 NaN -0.016093 -0.293432  0.342261 -0.041579  0.286858 -0.507258   \n",
       "ZN1 ZN2^2 NaN  0.214623 -0.304253  0.462458 -0.056364  0.439361 -0.561234   \n",
       "ZN2^3     NaN  0.477870 -0.253397  0.448283 -0.080771  0.478097 -0.484082   \n",
       "\n",
       "                AGE       DIS       RAD  ...     ZN0^3  ZN0^2 ZN1  ZN0^2 ZN2  \\\n",
       "1               NaN       NaN       NaN  ...       NaN        NaN        NaN   \n",
       "CRIM       0.398427 -0.409130  0.658170  ...  0.323198  -0.289927   0.515537   \n",
       "ZN        -0.569498  0.670739 -0.332850  ... -0.350266  -0.091572  -0.429276   \n",
       "INDUS      0.666270 -0.729739  0.572268  ...  0.407733  -0.012417   0.624719   \n",
       "CHAS       0.078795 -0.106944  0.004661  ... -0.108271  -0.010076  -0.126720   \n",
       "...             ...       ...       ...  ...       ...        ...        ...   \n",
       "ZN0 ZN2^2  0.558034 -0.485305  0.467276  ...  0.411844  -0.052867   0.936672   \n",
       "ZN1^3     -0.342146  0.350005 -0.463462  ... -0.187553   0.711408  -0.390007   \n",
       "ZN1^2 ZN2  0.409186 -0.284093  0.081219  ...  0.258771   0.577637   0.564306   \n",
       "ZN1 ZN2^2  0.497390 -0.405439  0.247726  ...  0.284099   0.307160   0.754920   \n",
       "ZN2^3      0.451311 -0.412170  0.375762  ...  0.268354  -0.109420   0.822817   \n",
       "\n",
       "           ZN0 ZN1^2  ZN0 ZN1 ZN2  ZN0 ZN2^2     ZN1^3  ZN1^2 ZN2  ZN1 ZN2^2  \\\n",
       "1                NaN          NaN        NaN       NaN        NaN        NaN   \n",
       "CRIM       -0.445567     0.101581   0.514396 -0.529322  -0.016093   0.214623   \n",
       "ZN          0.061411    -0.349521  -0.336015  0.209895  -0.293432  -0.304253   \n",
       "INDUS      -0.224279     0.446052   0.547373 -0.399147   0.342261   0.462458   \n",
       "CHAS        0.017174    -0.066676  -0.098913  0.031086  -0.041579  -0.056364   \n",
       "...              ...          ...        ...       ...        ...        ...   \n",
       "ZN0 ZN2^2  -0.244088     0.659143   1.000000 -0.383285   0.533159   0.807586   \n",
       "ZN1^3       0.928230     0.251364  -0.383285  1.000000   0.419015   0.068801   \n",
       "ZN1^2 ZN2   0.535625     0.962848   0.533159  0.419015   1.000000   0.887052   \n",
       "ZN1 ZN2^2   0.196187     0.920995   0.807586  0.068801   0.887052   1.000000   \n",
       "ZN2^3      -0.249773     0.568925   0.966147 -0.339574   0.468602   0.780267   \n",
       "\n",
       "              ZN2^3  \n",
       "1               NaN  \n",
       "CRIM       0.477870  \n",
       "ZN        -0.253397  \n",
       "INDUS      0.448283  \n",
       "CHAS      -0.080771  \n",
       "...             ...  \n",
       "ZN0 ZN2^2  0.966147  \n",
       "ZN1^3     -0.339574  \n",
       "ZN1^2 ZN2  0.468602  \n",
       "ZN1 ZN2^2  0.780267  \n",
       "ZN2^3      1.000000  \n",
       "\n",
       "[560 rows x 560 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_poly.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Showing the Polynomial Features and their coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coeff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Features</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM^2 NOX</th>\n",
       "      <td>-2.449692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM DIS ZN2</th>\n",
       "      <td>-1.258648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM ZN0 ZN2</th>\n",
       "      <td>-0.961161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD ZN0^2</th>\n",
       "      <td>-0.947691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIM NOX ZN2</th>\n",
       "      <td>-0.910405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX RAD ZN1</th>\n",
       "      <td>0.596884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM RAD ZN2</th>\n",
       "      <td>0.732233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN2^2</th>\n",
       "      <td>0.983744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN AGE</th>\n",
       "      <td>1.008311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM^2 ZN2</th>\n",
       "      <td>1.320189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Coeff\n",
       "Features              \n",
       "CRIM^2 NOX   -2.449692\n",
       "RM DIS ZN2   -1.258648\n",
       "RM ZN0 ZN2   -0.961161\n",
       "RAD ZN0^2    -0.947691\n",
       "CRIM NOX ZN2 -0.910405\n",
       "...                ...\n",
       "NOX RAD ZN1   0.596884\n",
       "RM RAD ZN2    0.732233\n",
       "ZN2^2         0.983744\n",
       "ZN AGE        1.008311\n",
       "RM^2 ZN2      1.320189\n",
       "\n",
       "[560 rows x 1 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features = pd.DataFrame(\n",
    "    {\n",
    "        'Features': x_train_poly.columns,\n",
    "        'Coeff': reg.coef_\n",
    "    }\n",
    ").set_index('Features').sort_values('Coeff')\n",
    "\n",
    "df_features[df_features['Coeff'].abs() > 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 790.5744081910345, tolerance: 17.210957\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge, Lasso\n",
    "\n",
    "# reg = Ridge(fit_intercept=False, solver='svd', alpha=1)\n",
    "reg = Lasso(fit_intercept=False)\n",
    "reg.fit(x_train_poly, y_train)\n",
    "\n",
    "y_test_pred = reg.predict(x_test_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Regressor = 0.725 vs Baseline = -0.0\n",
      "MAE Regressor = 2.879 vs Baseline = 6.9\n",
      "MSE Regressor = 27.113 vs Baseline = 99.2\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(\n",
    "    'R2 Regressor = {:.3f} vs Baseline = {:.1f}'.format(\n",
    "        r2_score(y_test, y_test_pred), \n",
    "        r2_score(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    'MAE Regressor = {:.3f} vs Baseline = {:.1f}'.format(\n",
    "        mean_absolute_error(y_test, y_test_pred), \n",
    "        mean_absolute_error(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\n",
    "    'MSE Regressor = {:.3f} vs Baseline = {:.1f}'.format(\n",
    "        mean_squared_error(y_test, y_test_pred), \n",
    "        mean_squared_error(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coeff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Features</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM^2</th>\n",
       "      <td>-0.018273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS RM TAX</th>\n",
       "      <td>-0.012368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM^2 ZN2</th>\n",
       "      <td>-0.011554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS AGE ZN2</th>\n",
       "      <td>-0.011163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS RAD^2</th>\n",
       "      <td>-0.010329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.017263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS RAD ZN2</th>\n",
       "      <td>0.020347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN1</th>\n",
       "      <td>0.026464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM^3</th>\n",
       "      <td>0.054097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.183862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>353 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Coeff\n",
       "Features              \n",
       "CRIM^2       -0.018273\n",
       "CHAS RM TAX  -0.012368\n",
       "RM^2 ZN2     -0.011554\n",
       "CHAS AGE ZN2 -0.011163\n",
       "DIS RAD^2    -0.010329\n",
       "...                ...\n",
       "TAX           0.017263\n",
       "DIS RAD ZN2   0.020347\n",
       "ZN1           0.026464\n",
       "RM^3          0.054097\n",
       "AGE           0.183862\n",
       "\n",
       "[353 rows x 1 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features = pd.DataFrame(\n",
    "    {\n",
    "        'Features': x_train_poly.columns,\n",
    "        'Coeff': reg.coef_\n",
    "    }\n",
    ").set_index('Features').sort_values('Coeff')\n",
    "\n",
    "df_features[df_features['Coeff'].abs() > 0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularized Linear Model (Lasso + Cross Validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "alphas = np.logspace(-6, 6, 50)\n",
    "\n",
    "reg = LassoCV(alphas=alphas, fit_intercept=False, cv=5, n_jobs=5)\n",
    "\n",
    "import warnings\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "warnings.simplefilter(\"always\", ConvergenceWarning) \n",
    "\n",
    "with warnings.catch_warnings(record=True) as w:\n",
    "    reg.fit(x_train_poly, y_train)\n",
    "\n",
    "y_train_pred = reg.predict(x_train_poly)\n",
    "y_test_pred = reg.predict(x_test_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.000001, 1,000,000'"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'{alphas.min():.6f}, {alphas.max():,.0f}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LassoCV: Chosen alpha = 68.66488450042998\n"
     ]
    }
   ],
   "source": [
    "print(f\"LassoCV: Chosen alpha = {reg.alpha_}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regularization Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:27: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 8))\n",
    "\n",
    "n_folds = reg.mse_path_.shape[1]\n",
    "\n",
    "mse_mean = reg.mse_path_.mean(axis=1)\n",
    "mse_std = reg.mse_path_.std(axis=1)\n",
    "mse_std_error = mse_std / np.sqrt(n_folds)\n",
    "\n",
    "pd.DataFrame(\n",
    "    {\n",
    "        'alpha': reg.alphas_,\n",
    "        'Mean MSE': mse_mean,\n",
    "        'Upper Bound MSE': mse_mean + 1.96 * mse_std_error,\n",
    "        'Lower Bound  MSE': mse_mean - 1.96 * mse_std_error,\n",
    "    }\n",
    ").set_index('alpha')[['Mean MSE', 'Upper Bound MSE', 'Lower Bound  MSE']].plot(\n",
    "    title='Regularization plot (MSE vs alpha)', marker='.', logx=True, ax=ax\n",
    ")\n",
    "\n",
    "plt.fill_between(reg.alphas_, mse_mean + 1.96 * mse_std_error, mse_mean - 1.96 * mse_std_error, alpha=0.1)\n",
    "\n",
    "ax.axvline(reg.alpha_,  linestyle='--', color='k', alpha=0.75,)\n",
    "\n",
    "ax.set_xlabel('Alpha')\n",
    "ax.set_ylabel('Mean Squared Error')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# n_folds equals to 5 here\n",
    "n_folds = reg.mse_path_.shape[1]\n",
    "\n",
    "# Calculate the mean and standard error for MSEs\n",
    "mse_mean = reg.mse_path_.mean(axis=1)\n",
    "mse_std = reg.mse_path_.std(axis=1)\n",
    "# Std Error = Std Deviation / SQRT(number of samples)\n",
    "mse_std_error = mse_std / np.sqrt(n_folds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Squared Error')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 8))\n",
    "\n",
    "# We multiply by 1.96 for a 95% Confidence Interval\n",
    "pd.DataFrame(\n",
    "    {\n",
    "        'alpha': reg.alphas_,\n",
    "        'Mean MSE': mse_mean,\n",
    "        'Upper Bound MSE': mse_mean + 1.96 * mse_std_error,\n",
    "        'Lower Bound MSE': mse_mean - 1.96 * mse_std_error,\n",
    "    }\n",
    ").set_index('alpha')[\n",
    "    ['Mean MSE', 'Upper Bound MSE', 'Lower Bound MSE']\n",
    "].plot(\n",
    "    title='Regularization plot (MSE vs alpha)', \n",
    "    marker='.', logx=True, ax=ax\n",
    ")\n",
    "\n",
    "# Color the confidence interval \n",
    "plt.fill_between(\n",
    "    reg.alphas_, \n",
    "    mse_mean + 1.96 * mse_std_error, \n",
    "    mse_mean - 1.96 * mse_std_error, \n",
    ")\n",
    "\n",
    "# Print a vertical line for the chosen alpha\n",
    "ax.axvline(reg.alpha_, linestyle='--', color='k')\n",
    "ax.set_xlabel('Alpha')\n",
    "ax.set_ylabel('Mean Squared Error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MSE, MAE and R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Regressor = -0.134 vs Baseline = -0.0\n",
      "MAE Regressor = 3.943 vs Baseline = 6.9\n",
      "MSE Regressor = 111.683 vs Baseline = 99.2\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(\n",
    "    'R2 Regressor = {:.3f} vs Baseline = {:.1f}'.format(\n",
    "        r2_score(y_test, y_test_pred), \n",
    "        r2_score(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    'MAE Regressor = {:.3f} vs Baseline = {:.1f}'.format(\n",
    "        mean_absolute_error(y_test, y_test_pred), \n",
    "        mean_absolute_error(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n",
    "\n",
    "print(\n",
    "    'MSE Regressor = {:.3f} vs Baseline = {:.1f}'.format(\n",
    "        mean_squared_error(y_test, y_test_pred), \n",
    "        mean_squared_error(y_test, y_test_baselin)\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features + Coefficients "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coeff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Features</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RAD TAX</th>\n",
       "      <td>-0.001212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS NOX TAX</th>\n",
       "      <td>-0.001069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS^2 RAD</th>\n",
       "      <td>-0.000876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIM DIS TAX</th>\n",
       "      <td>-0.000486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIM RM ZN1</th>\n",
       "      <td>-0.000463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN TAX</th>\n",
       "      <td>0.001189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM^2 ZN1</th>\n",
       "      <td>0.001321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS TAX</th>\n",
       "      <td>0.002115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM ZN1</th>\n",
       "      <td>0.003271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIM CHAS TAX</th>\n",
       "      <td>0.005300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>176 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Coeff\n",
       "Features               \n",
       "RAD TAX       -0.001212\n",
       "INDUS NOX TAX -0.001069\n",
       "INDUS^2 RAD   -0.000876\n",
       "CRIM DIS TAX  -0.000486\n",
       "CRIM RM ZN1   -0.000463\n",
       "...                 ...\n",
       "ZN TAX         0.001189\n",
       "RM^2 ZN1       0.001321\n",
       "INDUS TAX      0.002115\n",
       "RM ZN1         0.003271\n",
       "CRIM CHAS TAX  0.005300\n",
       "\n",
       "[176 rows x 1 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features = pd.DataFrame(\n",
    "    {\n",
    "        'Features': x_train_poly.columns,\n",
    "        'Coeff': reg.coef_\n",
    "    }\n",
    ").set_index('Features').sort_values('Coeff')\n",
    "\n",
    "df_features[df_features['Coeff'].abs() > 0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: This function is deprecated. Please call randint(0, 30 + 1) instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "df_noisy = pd.DataFrame(\n",
    "    {\n",
    "        'x': np.random.random_integers(0, 30, size=150),\n",
    "        'noise': np.random.normal(loc=0.0, scale=5.0, size=150)\n",
    "    }\n",
    ")\n",
    "\n",
    "df_noisy['y'] = df_noisy['x'] + df_noisy['noise']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa37398a240>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_noisy.plot(\n",
    "    kind='scatter', x='x', y='y'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import BayesianRidge\n",
    "    \n",
    "lr = LinearRegression()\n",
    "br = BayesianRidge()\n",
    "\n",
    "lr.fit(df_noisy[['x']], df_noisy['y'])\n",
    "df_noisy['y_lr_pred'] = lr.predict(df_noisy[['x']])\n",
    "\n",
    "\n",
    "br.fit(df_noisy[['x']], df_noisy['y'])\n",
    "df_noisy['y_br_pred'], df_noisy['y_br_std'] = br.predict(df_noisy[['x']], return_std=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:38: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(16, 6), sharex=True, sharey=True)\n",
    "\n",
    "\n",
    "df_noisy.sort_values('x').plot(\n",
    "    title='Data', kind='scatter', x='x', y='y', ax=axs[0]\n",
    ")\n",
    "    \n",
    "df_noisy.sort_values('x').plot(\n",
    "    kind='scatter', x='x', y='y', ax=axs[1], marker='o', alpha=0.25\n",
    ")\n",
    "\n",
    "axs[2].fill_between(\n",
    "    df_noisy.sort_values('x')['x'], \n",
    "    df_noisy.sort_values('x')['y_br_pred'] - 1.96 * df_noisy.sort_values('x')['y_br_std'], \n",
    "    df_noisy.sort_values('x')['y_br_pred'] + 1.96 * df_noisy.sort_values('x')['y_br_std'],\n",
    "    color=\"k\", alpha=0.2, label=\"Predictions +/- 1.96 * Std Dev\"\n",
    ")\n",
    "\n",
    "df_noisy.sort_values('x').plot(\n",
    "    kind='scatter', x='x', y='y', ax=axs[2], marker='o', alpha=0.25\n",
    ")\n",
    "\n",
    "df_noisy.sort_values('x').plot(\n",
    "    title='LinearRegression', kind='scatter', x='x', y='y_lr_pred', \n",
    "    ax=axs[1], marker='o', color='k', label='Predictions'\n",
    ")\n",
    "\n",
    "df_noisy.sort_values('x').plot(\n",
    "    title='BayesianRidge', kind='scatter', x='x', y='y_br_pred', \n",
    "    ax=axs[2], marker='o', color='k', label='Predictions'\n",
    ")\n",
    "\n",
    "\n",
    "axs[1].legend()\n",
    "axs[2].legend()\n",
    "\n",
    "    \n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:35: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(16, 6), sharex=True, sharey=True)\n",
    "\n",
    "# We plot the data 3 times\n",
    "df_noisy.sort_values('x').plot(\n",
    "    title='Data', kind='scatter', x='x', y='y', ax=axs[0]\n",
    ")\n",
    "df_noisy.sort_values('x').plot(\n",
    "    kind='scatter', x='x', y='y', ax=axs[1], marker='o', alpha=0.25\n",
    ")\n",
    "df_noisy.sort_values('x').plot(\n",
    "    kind='scatter', x='x', y='y', ax=axs[2], marker='o', alpha=0.25\n",
    ")\n",
    "\n",
    "# Here we plot the Linear Regression predictions\n",
    "df_noisy.sort_values('x').plot(\n",
    "    title='LinearRegression', kind='scatter', x='x', y='y_lr_pred', \n",
    "    ax=axs[1], marker='o', color='k', label='Predictions'\n",
    ")\n",
    "\n",
    "# Here we plot the Bayesian Ridge predictions\n",
    "df_noisy.sort_values('x').plot(\n",
    "    title='BayesianRidge', kind='scatter', x='x', y='y_br_pred', \n",
    "    ax=axs[2], marker='o', color='k', label='Predictions'\n",
    ")\n",
    "\n",
    "# Here we plot the range around the expected values\n",
    "# We multiply by 1.96 for a 95% Confidence Interval\n",
    "axs[2].fill_between(\n",
    "    df_noisy.sort_values('x')['x'], \n",
    "    df_noisy.sort_values('x')['y_br_pred'] - 1.96 * df_noisy.sort_values('x')['y_br_std'], \n",
    "    df_noisy.sort_values('x')['y_br_pred'] + 1.96 * df_noisy.sort_values('x')['y_br_std'],\n",
    "    color=\"k\", alpha=0.2, label=\"Predictions +/- 1.96 * Std Dev\"\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
